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Proceedings Paper

Comparison of subband features for automatic indexing of scientific image databases
Author(s): Kathleen G. Perez-Lopez; Arun K. Sood
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Paper Abstract

Indexing of scientific image databases is a difficult task, due to their extraordinary sizes and to the complex nature of the visual information contained in them. This data volume and complexity require an automatic indexing scheme that will categorize this visual content; without it, the data will be essentially useless to scientists and medical doctors. A method for automatic indexing of scientific image databases is presented which involves a wavelet package decomposition of images in the frequency domain, resulting in a quad-tree of subbands. These subbands are regarded as realizations of random fields, and statistical measures are computed on them. One class of newly derived measures determines whether the subbands contain any significant organization of pixels beyond what chance would imply. If this is found to be true for a subband, its node is retained on an index tree, and other identifying measurements may be added. The structure of the resulting pruned subband tree constitutes the first level of index; the node statistics form a second indexing level. Results of a pilot study are reported; they suggest that further investigation of this approach is warranted.

Paper Details

Date Published: 1 April 1994
PDF: 11 pages
Proc. SPIE 2185, Storage and Retrieval for Image and Video Databases II, (1 April 1994); doi: 10.1117/12.171788
Show Author Affiliations
Kathleen G. Perez-Lopez, George Mason Univ. (United States)
Arun K. Sood, George Mason Univ. (United States)


Published in SPIE Proceedings Vol. 2185:
Storage and Retrieval for Image and Video Databases II
Carlton Wayne Niblack; Ramesh C. Jain, Editor(s)

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